Large language models (LLMs) have shown exceptional problem-solving abilities through Chain-of-Thought (CoT) prompting, where the solution is broken into concrete steps. Researchers are now exploring whether similar advancements can be applied to foundation models for robots.
The study, conducted by teams from the University of California, Berkeley, the University of Warsaw, and Stanford University, introduces “Embodied Chain-of-Thought Reasoning” (ECoT) for vision-language-action models (VLAs).
ECoT aims to enhance robot decision-making by enabling them to reason about tasks, sub-tasks, and their environment before taking action.
Robotic control policies aim to enable robots to perform complex tasks autonomously. Despite significant progress in developing end-to-end control models, these models often fail in novel situations requiring reasoning and planning.
Vision-language-action models (VLAs) have emerged as promising tools for creating more general-purpose robot control policies. VLAs leverage pre-trained large vision-language models (VLMs) to map image observations and natural language instructions to robot actions, showing impressive generalization to new objects and scenes.
Notable examples include the open-source project OpenVLA and Google DeepMind’s RT-X-2. However, VLAs currently lack the reasoning capabilities seen in their LLM counterparts, learning direct mappings from observations to actions without intermediate reasoning steps.
To address this gap, researchers propose integrating chain-of-thought reasoning into VLAs, which have proven effective in LLMs. By generating intermediate steps, LLMs can better map relationships between different parts of a problem and produce more accurate solutions.
The researchers believe VLAs can similarly benefit by training them to reason textually about their plan, environment, and motions. However, applying CoT techniques to robotics poses several challenges.
VLAs, relying on smaller, open-source VLMs, are not as adept at reasoning as larger LLMs. Additionally, robotic tasks require reasoning about the task, environment, and robot’s state, necessitating a grounding of reasoning in environmental perception.
To overcome these challenges, the researchers developed Embodied Chain-of-Thought (ECoT) reasoning for VLAs. ECoT combines semantic reasoning about tasks and sub-tasks with embodied reasoning about the environment and the robot’s state, including predicting object bounding boxes and understanding spatial relationships.
The goal is to encourage the model to reason through high-level steps of the task and ground this reasoning in lower-level features of the scene and robot state before predicting actions.
This approach involves creating a pipeline to generate synthetic training data using pre-trained object detectors, LLMs, and VLMs to annotate existing robot datasets with reasoning information.
The researchers tested ECoT on a robotic manipulation setup using OpenVLA, built on Llama-2 7B and the Prismatic VLM. They generated training examples using the Bridge v2 dataset, which includes numerous trajectories and object interactions on WidowX, a robot arm with six degrees of freedom.
To assess generalization capabilities, they designed tasks requiring the robot to handle new objects, scenes, viewpoints, and instructions not present in the training data. ECoT significantly improved OpenVLA’s performance, increasing task success rates by 28% compared to the baseline model, all without additional robot training data.
Beyond performance improvements, ECoT enhanced the transparency and traceability of the model’s decision-making process. The reasoning steps, expressed in natural language, made it easier to identify and correct errors.
This approach allows humans to interact with and modify the policy’s behavior using natural language feedback, rather than complex teleoperation equipment. ECoT is part of a broader effort to integrate foundation models into robotic control systems, leveraging LLMs and VLMs’ ability to process large amounts of unlabeled data.
As the industry progresses, foundation models optimized for robotics are expected to fill existing gaps in robotic systems, advancing their capabilities further.
